Inverse text normalization (ITN) is used to convert the spoken form output of
an automatic speech recognition (ASR) system to a written form. Traditional
handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile
neural modeling approaches require quality large-scale spoken-written pair
examples in the same or similar domain as the ASR system (in-domain data), to
train. Both these approaches require costly and complex annotations. In this
paper, we present a data augmentation technique that effectively generates rich
spoken-written numeric pairs from out-of-domain textual data with minimal human
annotation. We empirically demonstrate that ITN model trained using our data
augmentation technique consistently outperform ITN model trained using only
in-domain data across all numeric surfaces like cardinal, currency, and
fraction, by an overall accuracy of 14.44%.

本文介绍一种数据增强技术，可有效地从领域外的文本数据生成丰富的口头书面数字对，可用于逆文本规范化模型的训练，通过在所有数字表面上的总精度提高了 14.44％，表明了在数据量和成本等方面的优势。